Untargeted Metabolomics Screen of Mid-pregnancy Maternal Serum and Autism in Offspring.
Adult
Air Pollution
Autism Spectrum Disorder
/ diagnosis
Bile Acids and Salts
/ metabolism
Brain
/ growth & development
Child
Child, Preschool
Female
Glycosphingolipids
/ metabolism
Humans
Male
Maternal Exposure
Metabolomics
Mothers
Polysaccharides
/ metabolism
Pregnancy
Pregnancy Trimester, Second
/ blood
Pyrimidines
/ metabolism
Steroids
/ metabolism
autism
high-resolution metabolomics
mid-pregnancy serum
steroid hormones
Journal
Autism research : official journal of the International Society for Autism Research
ISSN: 1939-3806
Titre abrégé: Autism Res
Pays: United States
ID NLM: 101461858
Informations de publication
Date de publication:
08 2020
08 2020
Historique:
received:
25
07
2019
revised:
24
03
2020
accepted:
15
04
2020
pubmed:
5
6
2020
medline:
10
4
2021
entrez:
5
6
2020
Statut:
ppublish
Résumé
Discovering pathophysiologic networks in a blood-based approach may help to generate valuable tools for early treatment or preventive measures in autism. To date targeted or untargeted metabolomics approaches to identify metabolic features and pathways affecting fetal neurodevelopment have rarely been applied to pregnancy samples, that is, an early period potentially relevant for the development of autism spectrum disorders (ASD). We conducted a population-based study relying on autism diagnoses retrieved from California Department of Developmental Services record. After linking cases to and sampling controls from birth certificates, we retrieved stored maternal mid-pregnancy serum samples collected as part of the California Prenatal Screening Program from the California Biobank for children born 2004 to 2010 in the central valley of California. We retrieved serum for 52 mothers whose children developed autism and 62 population controls originally selected from all eligible children matched by birth year and child's sex. Also, we required that these mothers were relatively low or unexposed to air pollution and select pesticides during early pregnancy. We identified differences in metabolite levels in several metabolic pathways, including glycosphingolipid biosynthesis and metabolism, N-glycan and pyrimidine metabolism, bile acid pathways and, importantly, C21-steroid hormone biosynthesis and metabolism. Disturbances in these pathways have been shown to be relevant for neurodevelopment in rare genetic syndromes or implicated in previous studies of autism. This study provides new insight into maternal mid-pregnancy metabolic features possibly related to the development of autism and an incentive to explore whether these pathways and metabolites are useful for early diagnosis, treatment, or prevention. LAY SUMMARY: This study found that in mid-pregnancy the blood of mothers who give birth to a child that develops autism has some characteristic features that are different from those of blood samples taken from control mothers. These features are related to biologic mechanisms that can affect fetal brain development. In the future, these insights may help identify biomarkers for early autism diagnosis and treatment or preventive measures. Autism Res 2020, 13: 1258-1269. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.
Substances chimiques
Bile Acids and Salts
0
Glycosphingolipids
0
Polysaccharides
0
Pyrimidines
0
Steroids
0
pyrimidine
K8CXK5Q32L
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1258-1269Subventions
Organisme : NIEHS NIH HHS
ID : R21ES022389
Pays : United States
Organisme : NIEHS NIH HHS
ID : R21ES025558
Pays : United States
Organisme : NIEHS NIH HHS
ID : R21ES25573
Pays : United States
Informations de copyright
© 2020 International Society for Autism Research, Wiley Periodicals, Inc.
Références
Adams, J. B., Audhya, T., McDonough-Means, S., Rubin, R. A., Quig, D., Geis, E., et al. (2011). Nutritional and metabolic status of children with autism vs. Neurotypical children, and the association with autism severity. Nutrition & Metabolism (London), 8, 34.
Alabdali, A., Al-Ayadhi, L., & El-Ansary, A. (2014). Association of social and cognitive impairment and biomarkers in autism spectrum disorders. Journal of Neuroinflammation, 11, 4.
Ali, A., Cui, X. Y., Alexander, S., & Eyles, D. (2018). The placental immune response is dysregulated developmentally vitamin d deficient rats: Relevance to autism. Journal of Steroid Biochemistry, 180, 73-80.
Andersen, S. L., Andersen, S., Vestergaard, P., & Olsen, J. (2018). Maternal thyroid function in early pregnancy and child neurodevelopmental disorders: A danish nationwide case-cohort study. Thyroid, 28, 537-546.
Anderson, G. M. (2002). Genetics of childhood disorders: Xlv. Autism, part 4: Serotonin in autism. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1513-1516.
Bagga, R., Vasishta, K., Majumdar, S., & Garg, S. K. (1990). Correlation between human placental-lactogen levels and glucose-metabolism in pregnant-women with intrauterine growth-retardation. Australian and New Zealand Journal of Obstetrics and Gynaecology, 30, 310-313.
Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., … Dowling, N. F. (2018). Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR Surveillance Summaries, 67, 1-23.
Baron-Cohen, S., Auyeung, B., Norgaard-Pedersen, B., Hougaard, D. M., Abdallah, M. W., Melgaard, L., et al. (2015). Elevated fetal steroidogenic activity in autism. Molecular Psychiatry, 20, 369-376.
Blatt, G. J., & Fatemi, S. H. (2011). Alterations in gabaergic biomarkers in the autism brain: Research findings and clinical implications. Anatomical Record (Hoboken), 294, 1646-1652.
Brecevic, L., Rincic, M., Krsnik, Z., Sedmak, G., Hamid, A. B., Kosyakova, N., et al. (2015). Association of new deletion/duplication region at chromosome 1p21 with intellectual disability, severe speech deficit and autism spectrum disorder-like behavior: An all-in approach to solving the dpyd enigma. Translational Neuroscience, 6, 59-86.
Bronson, S. L., & Bale, T. L. (2016). The placenta as a mediator of stress effects on neurodevelopmental reprogramming. Neuropsychopharmacology, 41, 207-218.
Brown, C. M., & Austin, D. W. (2011). Autistic disorder and phospholipids: A review. Prostaglandins, Leukotrienes, and Essential Fatty Acids, 84, 25-30.
Brunton, P. J. (2016). Neuroactive steroids and stress axis regulation: Pregnancy and beyond. The Journal of Steroid Biochemistry and Molecular Biology, 160, 160-168.
Brussel, W., van Kuilenburg, A. B., & Janssens, P. M. (2006). A neonate with recurrent vomiting and generalized hypotonia diagnosed with a deficiency of dihydropyrimidine dehydrogenase. Nucleosides, Nucleotides & Nucleic Acids, 25, 1099-1102.
Catalano, P. M., Huston, L., Amini, S. B., & Kalhan, S. C. (1999). Longitudinal changes in glucose metabolism during pregnancy in obese women with normal glucose tolerance and gestational diabetes mellitus. American Journal of Obstetrics and Gynecology, 180, 903-916.
Croen, L. A., Grether, J. K., & Selvin, S. (2002). Descriptive epidemiology of autism in a california population: Who is at risk? Journal of Autism and Developmental Disorders, 32, 217-224.
Cunningham, G. C., & Tompkinison, D. G. (1999). Cost and effectiveness of the california triple marker prenatal screening program. Genetics in Medicine, 1, 199-206.
Dietert, R. R., Dietert, J. M., & Dewitt, J. C. (2011). Environmental risk factors for autism. Emerging Health Threats Journal, 4, 7111.
Duggleby, S. L., & Jackson, A. A. (2002). Protein, amino acid and nitrogen metabolism during pregnancy: How might the mother meet the needs of her fetus? Current Opinion in Clinical Nutrition & Metabolic Care, 5, 503-509.
Farooqui, A. A., Liss, L., & Horrocks, L. A. (1988). Neurochemical aspects of alzheimers-disease-involvement of membrane phospholipids. Metabolic Brain Disease, 3, 19-35.
Freeze, H. H., Eklund, E. A., Ng, B. G., & Patterson, M. C. (2015). Neurological aspects of human glycosylation disorders. Annual Review of Neuroscience, 38, 105-125.
Fumagalli, M., Lecca, D., Abbracchio, M. P., & Ceruti, S. (2017). Pathophysiological role of purines and pyrimidines in neurodevelopment: Unveiling new pharmacological approaches to congenital brain diseases. Frontiers in Pharmacology, 8, 941.
Go, Y. M., Walker, D. I., Liang, Y., Uppal, K., Soltow, Q. A., Tran, V., … Jones, D. P. (2015). Reference standardization for mass spectrometry and high-resolution metabolomics applications to exposome research. Toxicological Sciences, 148, 531-543.
Goeden, N., Velasquez, J., Arnold, K. A., Chan, Y., Lund, B. T., Anderson, G. M., & Bonnin, A. (2016). Maternal inflammation disrupts fetal neurodevelopment via increased placental output of serotonin to the fetal brain. The Journal of Neuroscience, 36, 6041-6049.
Grove, J., Ripke, S., Als, T. D., Mattheisen, M., Walters, R. K., Won, H., et al. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature Genetics, 51, 431-444.
Grunewald, S. (2007). Congenital disorders of glycosylation: Rapidly enlarging group of (neuro)metabolic disorders. Early Human Development, 83, 825-830.
Heazell, A. E., Bernatavicius, G., Warrander, L., Brown, M. C., & Dunn, W. B. (2012). A metabolomic approach identifies differences in maternal serum in third trimester pregnancies that end in poor perinatal outcome. Reproductive Sciences, 19, 863-875.
Horgan, R. P., Broadhurst, D. I., Dunn, W. B., Brown, M., Heazell, A. E., Kell, D. B., et al. (2010). Changes in the metabolic footprint of placental explant-conditioned medium cultured in different oxygen tensions from placentas of small for gestational age and normal pregnancies. Placenta, 31, 893-901.
Horgan, R. P., Broadhurst, D. I., Walsh, S. K., Dunn, W. B., Brown, M., Roberts, C. T., … Kenny, L. C. (2011). Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy. Journal of Proteome Research, 10, 3660-3673.
Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118-127.
Kalhan, S. C., Rossi, K. Q., Gruca, L. L., Super, D. M., & Savin, S. M. (1998). Relation between transamination of branched-chain amino acids and urea synthesis: Evidence from human pregnancy. American Journal of Physiology Endocrinology and Metabolism, 275, E423-E431.
Kalkhoff, R.K., & Kim, H.J. (1978). The influence of hormonal changes of pregnancy on maternal metabolism. Ciba Foundation symposium: 29-56. Basel, Switzerland: Novartis Foundation.
Kanungo, S., Soares, N., He, M., & Steiner, R. D. (2013). Sterol metabolism disorders and neurodevelopment-an update. Developmental Disabilities Research Reviews, 17, 197-210.
Kenny, L. C., Broadhurst, D. I., Dunn, W., Brown, M., North, R. A., McCowan, L., … Screening for Pregnancy Endpoints Consortium. (2010). Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers. Hypertension, 56, 741-749.
Koren, O., Goodrich, J. K., Cullender, T. C., Spor, A., Laitinen, K., Backhed, H. K., et al. (2012). Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell, 150, 470-480.
Kurochkin, I., Khrameeva, E., Tkachev, A., Stepanova, V., Vanyushkina, A., Stekolshchikova, E., … Khaitovich, P. (2019). Metabolome signature of autism in the human prefrontal cortex. Communications Biology, 2, 234.
Le Cao, K. A., Gonzalez, I., & Dejean, S. (2009). Integromics: An r package to unravel relationships between two omics datasets. Bioinformatics, 25, 2855-2856.
Li, S., Park, Y., Duraisingham, S., Strobel, F. H., Khan, N., Soltow, Q. A., … Pulendran, B. (2013). Predicting network activity from high throughput metabolomics. PLoS Computational Biology, 9, e1003123.
Ling, C., Liew, Z., von Ehrenstein, O. S., Heck, J. E., Park, A. S., Cui, X., … Ritz, B. (2018). Prenatal exposure to ambient pesticides and preterm birth and term low birthweight in agricultural regions of california. Toxics, 6. http://dx.doi.org/10.3390/toxics6030041.
Loffler, M., Carrey, E. A., & Zameitat, E. (2015). Orotic acid, more than just an intermediate of pyrimidine de novo synthesis. Journal of Genetics and Genomics, 42, 207-219.
Loi, M. (2006). Lowe syndrome. Orphanet Journal of Rare Diseases, 1, 16.
Luan, H., Meng, N., Liu, P., Feng, Q., Lin, S., Fu, J., … Wang, J. (2014). Pregnancy-induced metabolic phenotype variations in maternal plasma. Journal of Proteome Research, 13, 1527-1536.
Maitre, L., Villanueva, C. M., Lewis, M. R., Ibarluzea, J., Santa-Marina, L., Vrijheid, M., et al. (2016). Maternal urinary metabolic signatures of fetal growth and associated clinical and environmental factors in the inma study. BMC Medicine, 14, 14. http://dx.doi.org/10.1186/s12916-016-0706-3.
Melcangi, R. C., Garcia-Segura, L. M., & Mensah-Nyagan, A. G. (2008). Neuroactive steroids: State of the art and new perspectives. Cellular and Molecular Life Sciences, 65, 777-797.
Mussap, M., Noto, A., & Fanos, V. (2016). Metabolomics of autism spectrum disorders: Early insights regarding mammalian-microbial cometabolites. Expert Review of Molecular Diagnostics, 16, 869-881.
Naviaux, J. C., Schuchbauer, M. A., Li, K., Wang, L., Risbrough, V. B., Powell, S. B., & Naviaux, R. K. (2014). Reversal of autism-like behaviors and metabolism in adult mice with single-dose antipurinergic therapy. Translational Psychiatry, 4, e400.
Nelson, K. B., Grether, J. K., Croen, L. A., Dambrosia, J. M., Dickens, B. F., Jelliffe, L. L., … Phillips, T. M. (2001). Neuropeptides and neurotrophins in neonatal blood of children with autism or mental retardation. Annals of Neurology, 49, 597-606.
Nguyen, P. N., Billiards, S. S., Walker, D. W., & Hirst, J. J. (2003). Changes in 5alpha-pregnane steroids and neurosteroidogenic enzyme expression in the perinatal sheep. Pediatric Research, 53, 956-964.
Nguyen, P. N., Yan, E. B., Castillo-Melendez, M., Walker, D. W., & Hirst, J. J. (2004). Increased allopregnanolone levels in the fetal sheep brain following umbilical cord occlusion. The Journal of Physiology, 560, 593-602.
Park, B. Y., Lee, B. K., Burstyn, I., Tabb, L. P., Keelan, J. A., Whitehouse, A. J. O., et al. (2017). Umbilical cord blood androgen levels and asd-related phenotypes at 12 and 36 months in an enriched risk cohort study. Molecular Autism, 8.http://dx.doi.org/10.1186/s13229-017-0118-z.
Pasqualini, J. R. (1970). Metabolic conjugation and hydrolysis of steroid hormones in the fetoplacental unit. In W. Fishman (Ed.), Metabolic conjugation and metabolic hydrolysis (Vol. 2, pp. 173-174). Tufts University School of Medicine New England, Medical Center Hospitals Boston, Massachusetts : Elsevier Inc.
Pierce, K., Glatt, S. J., Liptak, G. S., & McIntyre, L. L. (2009). The power and promise of identifying autism early: Insights from the search for clinical and biological markers. Annals of Clinical Psychiatry, 21, 132-147.
Pinto, J., Barros, A. S., Domingues, M. R., Goodfellow, B. J., Galhano, E., Pita, C., et al. (2015). Following healthy pregnancy by nmr metabolomics of plasma and correlation to urine. Journal of Proteome Research, 14, 1263-1274.
Raghavan, R., Riley, A. W., Volk, H., Caruso, D., Hironaka, L., Sices, L., … Wang, X. (2018). Maternal multivitamin intake, plasma folate and vitamin b12 levels and autism spectrum disorder risk in offspring. Paediatric and Perinatal Epidemiology, 32, 100-111.
Rangel-Huerta, O. D., Gomez-Fernandez, A., de la Torre-Aguilar, M. J., Gil, A., Perez-Navero, J. L., Flores-Rojas, K., et al. (2019). Metabolic profiling in children with autism spectrum disorder with and without mental regression: Preliminary results from a cross-sectional case-control study. Metabolomics, 15, 99.
Rossignol, D. A., & Frye, R. E. (2012). Mitochondrial dysfunction in autism spectrum disorders: A systematic review and meta-analysis. Molecular Psychiatry, 17, 290-314.
Salas, S. P., Marshall, G., Gutierrez, B. L., & Rosso, P. (2006). Time course of maternal plasma volume and hormonal changes in women with preeclampsia or fetal growth restriction. Hypertension, 47, 203-208.
Sealey, L. A., Hughes, B. W., Sriskanda, A. N., Guest, J. R., Gibson, A. D., Johnson-Williams, L., … Bagasra, O. (2016). Environmental factors in the development of autism spectrum disorders. Environment International, 88, 288-298.
Simon-Manso, Y., Lowenthal, M. S., Kilpatrick, L. E., Sampson, M. L., Telu, K. H., Rudnick, P. A., et al. (2013). Metabolite profiling of a nist standard reference material for human plasma (srm 1950): Gc-ms, lc-ms, nmr, and clinical laboratory analyses, libraries, and web-based resources. Analytical Chemistry, 85, 11725-11731.
Sivan, E., & Boden, G. (2003). Free fatty acids, insulin resistance, and pregnancy. Current Diabetes Reports, 3, 319-322.
Uppal, K., Soltow, Q. A., Strobel, F. H., Pittard, W. S., Gernert, K. M., Yu T., & Jones D. P. (2013). xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data. BMC Bioinformatics, 14, 15.
Uppal, K., Walker, D. I., & Jones, D. P. (2017). Xmsannotator: An r package for network-based annotation of high-resolution metabolomics data. Analytical Chemistry, 89, 1063-1067.
Verbeek, E., Oliver, M. H., Waas, J. R., McLeay, L. M., Blache, D., & Matthews, L. R. (2012). Reduced cortisol and metabolic responses of thin ewes to an acute cold challenge in mid-pregnancy: Implications for animal physiology and welfare. PLoS One, 7, e37315.
von Ehrenstein, O. S., Ling, C., Cui, X., Cockburn, M., Park, A. S., Yu, F., et al. (2019). Prenatal and infant exposure to ambient pesticides and autism spectrum disorder in children: Population based case-control study. BMJ, 364, l962.
Vu, T. T., Hirst, J. J., Stark, M., Wright, I. M., Palliser, H. K., Hodyl, N., et al. (2009). Changes in human placental 5alpha-reductase isoenzyme expression with advancing gestation: Effects of fetal sex and glucocorticoid exposure. Reproduction, Fertility, and Development, 21, 599-607.
Walker, D. I., Lane, K. J., Liu, K., Uppal, K., Patton, A. P., Durant, J. L., et al. (2018). Metabolomic assessment of exposure to near-highway ultrafine particles. Journal of Exposure Science & Environmental Epidemiology, 29, 469-483.
Walker, D. I., Perry-Walker, K., Finnell, R. H., Pennell, K. D., Tran, V., May, R. C., … Jones, D. P. (2019). Metabolome-wide association study of anti-epileptic drug treatment during pregnancy. Toxicology and Applied Pharmacology, 363, 122-130.
Williams, M., Zhang, Z., Nance, E., Drewes, J. L., Lesniak, W. G., Singh, S., … Kannan, S. (2017). Maternal inflammation results in altered tryptophan metabolism in rabbit placenta and fetal brain. Developmental Neuroscience, 39, 399-412.
Wold, S., Sjostrom, M., & Eriksson, L. (2001). Pls-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109-130.
Yan, Q., Liew, Z., Uppal, K., Cui, X., Ling, C., Heck, J. E., … Ritz, B. (2019). Maternal serum metabolome and traffic-related air pollution exposure in pregnancy. Environment International, 130, 104872.
Yap, I. K. S., Angley, M., Veselkov, K. A., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2010). Urinary metabolic phenotyping differentiates children with autism from their unaffected siblings and age-matched controls. Journal of Proteome Research, 9, 2996-3004.
Yoo, H. (2015). Genetics of autism spectrum disorder: Current status and possible clinical applications. Experimental Neurobiology, 24, 257-272.
Yu, T., Park, Y., Johnson, J. M., & Jones, D. P. (2009). apLCMS-adaptive processing of high-resolution LC/MS data. Bioinformatics, 25, 1930-1936.